Attaining accurate estimation of such neighborhoods is a non-trivial task, due to the stochasticity pertaining to the ground-truth definition. Following a stochastic optimization, neighborhood formation is bounded to minimize the trajectory prediction errors, such that the underlying graph structure improves the social interaction modeling.
We develop neighborhood modeling mechanism based on Grid-LSTM [1]}, which is a grid mask that segments the environment into a regular-shaped grid. It encompasses the sharing mechanism between adjacent neighborhoods in the grid. This sharing determines how pedestrians social interaction is modeled in deep networks. Predicting pedestrians future trajectory can be used to further define future neighborhoods, thereby leading to a better understanding of how pedestrians are related and influenced by each other.
